• 디지털융복합연구
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• 제18권12호
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• pp.481-488
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• 2020

기호적 회귀분석 (Symbolic Regression)은 회귀분석에서 주어진 데이터에 대하여 종속변수와 독립변수들 사이의 관계를 설명할 수 있는 함수를 직접 생성하는 분석방법으로서 Genetic Programming 이 본 분야의 연구에 가장 선도적으로 적용되고 있으며, 고정된 모델로부터 매개변수들의 최적화를 추구하는 다른 회귀분석 알고리즘들에 비하여 해석이 가능한 모델을 직접 도출할 수 있다는 장점을 갖는다. 본 연구에서는 Coarse grained 병렬 모델에 기반한 Parellel Genetic Programming 을 이용한 symbolic regression 알고리즘을 제시하고 제시된 알고리즘을 PMLB 데이타에 적용하여 해당 알고리즘의 효용성을 분석하고자 한다.

• 한국의류학회지
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• 제22권6호
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• pp.781-792
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• 1998

The purpose of this study was to investigate the factors related to the propensity for symbolic consumption and the effects of materialism, reference group, and social stratification on the symbolic consumption in clothing. Data were administered to 957 adolescence in middle, high school, and college student living in Seoul, Chonju, Sunchon, Yousu, and Kwangyang from May to June 1997. For analysis of the data, frequencies, percentage, means, standard deviation, factor analysis, 1-test, one-way anomia, duncan's multiple range test, and multiple regression analysis were employed. The results of this study can be summarized asfollows. 1) Symbolic consumption, materialism, and reference group were found to have the significant differences according to social stratification groups by objectivemethod. The higher social stratification is, the higher symbolic consumption, materialism, and reference group were. 2) symbolic consumption were proven to have the significant differences according to materiaiism and reference group. The higher materialism and the influence of referencegroup indicated, the higher symbolic consumption. 3)according to the results of the regression analysis examining the relative influences of variables affecting symbolic consumption in clothing, the relative importance of the variables are in order of : the influences of the reference group, materialism, social stratification, status inconsistency type (occupation-income), and their explanatory power totalled 40.0%.

• 대한가정학회지
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• 제36권10호
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• pp.131-144
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• 1998

The purpose of this study was to examine the differences of symbolic consumption of adolescents, and the effects of demographic factors on the symbokic consumption in clothing. Data were administered to 957 adolescents in middle, high school, and college students living in Seoul, Chonju, Sunchon, Yousu, and Kwangyang from May to June 1997. For analysis of the data, factor analysis, t-test, one-way ANOCA, duncan's multiple range test, and multiple regression analysis were employed. The results of this study were summarized as follows. 1) Symbolic consumption in colthing were shown to have the significant differences accoding to age, gender, the level of urbanization, parent's education, father's occupation, social stratification groups. The higher the age, the level of urbanization, and parent's education, father's occupation, social stratification is, or the female, the higher is symbolic consumption in clothing. 2) According to the results of the regression analysis examining the rerlative influences of variables affecting symbolic consumption in clothing, the relative importance of the variables are in order of; income, gender, age, mother's education, residence, and their explanatory powere totalled 11.5%.

• Computers and Concrete
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• 제20권1호
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• pp.65-75
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• 2017

In this study, a novel application of symbolic regression (SR) is employed for the prediction of ultimate shear strength of steel fiber reinforced (SFRC) and glass fiber reinforced (GFRC) corbels without stirrups, for the first time in the literature. A database is created using the test results (42 tests) conducted by the authors of current paper as well as the previous studies available in the literature. A symbolic regression based empirical formulation is proposed using this database. The formulation is unique in a way that it has the capability to predict the shear strength of both SFRC and GFRC corbels. The performance of proposed model is tested against randomly selected testing set. Additionally, a parametric study with a wide range of variables is carried out to test the effect of each parameter on the shear strength. The results confirm the high prediction capacity of proposed model.

• 제어로봇시스템학회논문지
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• 제20권7호
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• pp.730-735
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• 2014

This paper introduces a GP (Genetic Programming) based robust technique for the prediction of a heavy rain newsflash. The nature of prediction for precipitation is very complex, irregular and highly fluctuating. Especially, the prediction of heavy precipitation is very difficult. Because not only it depends on various elements, such as location, season, time and geographical features, but also the case data is rare. In order to provide a robust model for precipitation prediction, a nonlinear and symbolic regression method using GP is suggested. The remaining part of the study is to evaluate the performance of prediction for a heavy rain newsflash using a GP based nonlinear regression technique in Korean regions. Analysis of the feature selection is executed and various fitness functions are proposed to improve performances. The KLAPS data of 2006-2010 is used for training and the data of 2011 is adopted for verification.

• 한국의류산업학회지
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• 제10권4호
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• pp.499-505
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• 2008

The Purpose of this study is to research the relations between brand attachment and brand loyalty depending on symbolic consumption propensity toward fashion goods. Subjects were 391 women in their twenties to fifties who live in Gyungsang Province and have purchased the fashion goods. Frequency analysis, reliability analysis, factor analysis, multiple regression analysis, and one-way layout variance analysis were conducted using SPSS 13.0 as data analysis. The findings from the analysis are described in the following: Uniqueness and materialism out of the symbolic consumption propensity toward fashion goods had positive effects on the elements of brand attachment such as love, care, and knowledge. Brand loyalty was influenced positively by social face sensitivity and materialism among symbolic consumption toward fashion goods.

• 한국의류학회지
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• 제28권12호
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• pp.1562-1570
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• 2004

본 연구는 제품의 기능적 측면보다는 제품이 지니고 있는 사회적, 주관적 의미를 더 중요하게 생각하는 소비지향적인 사회분위기 속에서, 이론적 연구와 예비조사결과를 토대로 물질주의 성향과 사회화대행자의 영향력에 따른 의복의 상징적 소비에 차이가 있는지를 알아보고자 하였다. 연구대상은 수도권에 거주하는 20대 여성 500명을 편의 추출하여 설문조사하였으며, 불성실한 응답을 제외한 426명을 SPSS통계 Package를 이용하여 요인분석, 신뢰도 검증, 분산분석, Duncan-test, 회귀분석 등을 실시하였다. 연구결과, 의복의 상징적 소비는 유행추구적 소비, 유명상표지향적 소비, 쾌락적 소비 요인으로 분류되었으며,20대 여성 소비자에게 의복은 기능적 측면으로서 뿐만 아니라 쾌락적 소비의 수단으로써 중요한 존재임을 알 수 있었다. 또한 물질주의 성향이 높을수록, 사회화대행자의 영향력을 많이 받을수록 의복의 상징적 소비와 하위요인의 성향도 높게 나타났다. 다중회귀분석 결과 의복의 상징적 소비에 영향을 미치는 가장 큰 요인은 준거집단의 영향이었으며, 물질주의성향과 대중매체의 영향도 큰 것으로 나타났다.

• Steel and Composite Structures
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• 제43권3호
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• pp.293-309
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• 2022

Accurate design models for predicting the shear resistance of headed studs in solid concrete slabs are essential for obtaining economical and safe steel-concrete composite structures. In this study, symbolic regression with genetic programming (GPSR) was applied to experimental data to formulate new descriptive equations for predicting the shear resistance of studs in solid slabs using both normal and lightweight concrete. The obtained GPSR-based nominal resistance equations demonstrated good agreement with the test results. The equations indicate that the stud shear resistance is insensitive to the secant modulus of elasticity of concrete, which has been included in many international standards following the pioneering work of Ollgaard et al. In contrast, it increases when the stud height-to-diameter ratio increases, which is not reflected by the design models in the current international standards. The nominal resistance equations were subsequently refined for use in design from reliability analyses to ensure that the target reliability index required by the Eurocodes was achieved. Resistance factors for the developed equations were also determined following US design practice. The stud shear resistance predicted by the proposed models was compared with the predictions from 13 existing models. The accuracy of the developed models exceeds the accuracy of the existing equations. The proposed models produce predictions that can be used with confidence in design, while providing significantly higher stud resistances for certain combinations of variables than those computed with the existing equations given by many standards.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2006년도 심포지엄 논문집 정보 및 제어부문
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• pp.270-272
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• 2006

In this paper, we discuss optimal design of Fuzzy Polynomial Neural Networks by means of Genetic Algorithms(GAs) using symbolic coding for non-linear data. One of the major subject of genetic algorithms is representation of chromosomes. The proposed model optimized by the means genetic algorithms which used symbolic code to represent chromosomes. The proposed gFPNN used a triangle and a Gaussian-like membership function in premise part of rules and design the consequent structure by constant and regression polynomial (linear, quadratic and modified quadratic) function between input and output variables. The performance of the proposed model is quantified through experimentation that exploits standard data already used in fuzzy modeling. These results reveal superiority of the proposed networks over the existing fuzzy and neural models.

• 대한가정학회지
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• 제42권8호
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• pp.123-132
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• 2004

The purpose of this research was to determine the relation between clothing evaluative criteria and personality types. Primary factor analysis was used to classify the clothing evaluative criteria and the continual scores of Myers Briggs Type Indicator (MBTI) were used for classifying the personality types. Correlation analysis and multiple regression analysis were used to determine the relation between the clothing evaluative criteria and the personality types. The data for this research were collected from questionnaires of 309 female university students in Seoul. In the results, the clothing evaluative criteria were grouped into 4 underlying dimensions： practical, situational, appearance producible and symbolic. Partially significant relations between the 4 clothing evaluative criteria and the 4 indicators of MBTI personality types were found through correlation analysis. Multiple regression analysis revealed that the variables explaining the dimension of Sensing and Intuition (SN) were appearance producible and symbolic clothing evaluative criteria; the appearance producible criterion had an inclination toward Intuition while the symbolic criterion had an inclination toward Sensing. The variable explaining the dimension of Judging and Perceiving (JP) was situational clothing evaluative criterion, which had an inclination toward Judging.